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dc.contributor.authorXiong, Weien_US
dc.contributor.authorHu, Hanpingen_US
dc.contributor.authorXiong, Naixueen_US
dc.contributor.authorYang, Laurence T.en_US
dc.contributor.authorPeng, Wen-Chihen_US
dc.contributor.authorWang, Xiaofeien_US
dc.contributor.authorQu, Yanzhenen_US
dc.description.abstractCloud computing represents a new paradigm where computing resources are offered as services in the world via communication Internet. As many new types of attacks are arising at a high frequency, the cloud computing services are exposed to an increasing amount of security threats. To reduce security risks, two approaches of the network traffic anomaly detection in cloud communications have been presented, which analyze dynamic characteristics of the network traffic based on the synergetic neural networks and the catastrophe theory. In the former approach, a synergetic dynamic equation with a group of the order parameters is used to describe the complex behaviors of the network traffic system in cloud communications. When this equation is evolved, only the order parameter determined by the primary factors can converge to 1. Then, the anomaly can be detected. In the latter approach; a catastrophe potential function is introduced to describe the catastrophe dynamic process of the network traffic in cloud communications. When anomalies occur, the state of the network traffic will deviate from the normal one. To assess the deviation, an index named as catastrophe distance is defined. The network traffic anomaly can be detected by the value of this index. We evaluate the performance of these two approaches using the standard Defense Advanced Research Projects Agency data sets. Experimental results show that our approaches can effectively detect the network traffic anomaly and achieve the high detection probability and the low false alarms rate. (C) 2013 Published by Elsevier Inc.en_US
dc.subjectAnomaly detectionen_US
dc.subjectCloud communicationen_US
dc.subjectNetwork trafficen_US
dc.subjectSynergetic neural networksen_US
dc.subjectCatastrophe theoryen_US
dc.subjectChaotic dynamicsen_US
dc.titleAnomaly secure detection methods by analyzing dynamic characteristics of the network traffic in cloud communication?en_US
dc.identifier.journalINFORMATION SCIENCESen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
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